If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Lectured in Philosophy at the Hebrew University In Jerusalem and became Associate Professor in 1957. Since 1957 he has also taught in the Department of History and Philosophy of Science. Joint author with Professor A. A. Fraenkel of "Foundations of Set Theory", to be published by the North-Holland Publishing Company in the series "Studies In Logic". Y. BAR-HILLEL SUMMARY "FOUR sources of inefficiencies in the process of literature searching are briefly described. An "Ideal" solution Is outlined as a frame of reference and its shortcomings discussed.
Marvin Lee Minsky was born in New York on 9th August, 1927. He received his B.A from Harvard in 1950 and Ph.D in Mathematics from Princeton in 1954. For the next three years he was a member of the Harvard University Society of Fellows, and in 1957-58 was staff member of the M.I.T. Lincoln Laboratories. At present he is Assistant Professor of Mathematics at M.I.T. where he is giving a course in Automata and Artificial Intelligence and is also staff member of the Research Laboratory of Electronics. Particular attention is given to processes involving pattern recognition, learning, planning ahead, and the use of analogies or?models!.
Reprinted front the PROCEEDINGS OF THE WESTERN JOINT COMPUTER CONFERENCE Los Angeles, California, March 1955 PRINTED IN THE U.S.A. E CONSIDER the process we call Pattern Recognition. By this we mean the extraction of the significant features of data from a background of irrelevant detail. What we are interested in is simulating this process on digital computers. We give examples on three levels of complexity corresponding to the subjects of the other three speakers here today. We examine in detail the problem on the second level, visual recognition of simple shapes.
A knowledge-based programming system can utilize a very-high-level self description to rewrite and improve itself. This paper presents a specification, in the very-high-level language V, of the rule compiler component of the CIII knowledgebased programming system. From this specification of part of itself, CIII produces an efficient program satisfying the specification. This represents a modest application of a machine intelligence system to a real programming problem, namely improving one of the programming environment's tools -- the rule compiler. The high-level description and the use of a programming knowledge base provide potential for system performance to improve with added knowledge.
Thomas G. Dicttcrich Stanford University Stanford, California 94305 Ryszard S. Michalski University of Illinois Urbana, Illinois 61801 The authors gratefully acknowledge the partial support of the NSF under grant MCS-82-05166 and of the Office of Naval Research under grant No. N00014-82-K-0l 86. A more general kind of sequence-prediction problem--the non-deterministic prediction problem--is defined, and a general methodology for its solution presented. The methodology, called SPARC, employs multiple description models to guide the search for plausible sequence-generating rules. Three different models are presented along with algorithms for instantiating them to discover rules. The instantiation process requires that the initial input sequence be substantially transformed to make explicit important features of the sequence.
Copyright 1979 William Kaufmann, Inc., all rights reserved. Reprinted by permission from The Handbook of Artificial Intelligence, Vol.II, No.10, Avron BAIrr and Edward A. Feigenbaum, 'EJs., 1979. Jorge Phillips' work was supported In part at the Stanford Al Lab (ARPA Order 2484, Contract MDA 903-76-C-0206). The views and conclusions of this document should not be Interpreted as necessarily representing the official policies, either express or implied, of the Defense Advanced Research Projects Agency, the National institutes of Health, or the United States Government. Copyright Notice: The material herein is copyright protected.
It is an extension of a longstanding effort to cultivate attention to ongoing laboratory research as a domain of explorations in artificial intelligence. Our major effort in this field had been in the DENDRNL project, with analytical organic chemistry as the object discipline. However, for reasons that will be elaborated, the focus is on programs to suggest experimentplanning sequences needed to solve a given structure, rather than on hypotheses about the structures themselves, which characterizes CENTRAL. Our primary motiva,don is to deepen our knowledge of the art and science of creating programs that reason with symbolic knowledge to aid.man problem solvers. The task domain--molecular genetics--serves as a rich intellectual and scientific environment in which to develop and test our ideas.
Mark.1 ctefik Aim 1A77 earn l 1 The use of a representation language involving schemata and associated derived models has been extended to include all aspects of domain knowledge and strategy and heuristic problem solving knowledge. This uniform representation will allow the extension of knowledge base management techniques for acquisition and retrieval of procedural knowledge. Introduction The design of MCLGEN is based on the proposition that successful problem solving in complicated domains requires the use of large amounts of domain specific knowledge. As a result, the representation of this knowledge is a problem of practical importance. The domain specific knowledge includes knowledge about the objects, the transformations applicable to the objects and information about use, effectiveness, and cost of the transformations.
Human scientists actively seek out information that bears on questions they have decided to pursue. They design experiments, explore the implications of the knowledge they have, refine their questions and test alternative ideas. Although many discoveries are the result of unexpected observations, these surprises take place in the context of an explicit pursuit of knowledge. Viewing scientific discovery as a kind of motivated action raises some basic issues common to goal-directed behavior generally: Where do desires (to know) come from? What are the actions that can be taken (to discover)?